The present invention relates to a disk reproducing apparatus using a constant angular velocity, and more particularly, to an adaptive equalizer for controlling a tap coefficient of a filter by assuming a recording density of each track.
Recently, according to the high density recording of magnetic recording media, e.g., hard disks and floppy disks, and optical recording media such as magneto-optic disks, compactsdisks and laser disks, there is an increasing problem of errors in the reproduced data, which is caused by increased interference between adjacent signals.
Generally, an equalizer is used for raising the recording density by eliminating the interference between adjacent signals. There are various kinds of equalizers, for example, a cosine equalizer adopting an analog method and a partial response maximum likelihood (PRML) equalizer and decision feedback equalizer (DFE) which adopt digital methods.
Meanwhile, the recording density is different even in one disk. That is, in the case of disk recording media using the constant angular velocity method, since the inner diameter of the disk is smaller than the outer, the relative recording density is increased for the inner diameter tracks. Thus, an adaptive equalizer is required for tracking and compensating the channel characteristics that change according to a change in the recording density.
The conventional adaptive equalizer is composed of a finite impulse response (FIR) filter which is a type of transverse filter, and has a transfer characteristic as given in the following equation (1). ##EQU1## where C.sub.k is a tap coefficient.
An error signal e.sub.k which is the difference between an interference-free reproduced information signal Y.sub.k obtained from the above equation (1) and reproduced data d.sub.k detected from the reproduced information signal is determined using the following equation (2). EQU e.sub.k =y.sub.k -d.sub.k ( 2)
The tap CO efficient of the equalizer is adjusted using the following equation (3) for a means square error to be minimized according to the adaptive algorithm. EQU C.sub.k+1 =.beta.C.sub.k +.mu.e.sub.k .gamma..sub.k ( 3)
Where .beta. is a leakage factor and .mu. is a step size. However, since the above equation (3) is a least mean sequence (LMS) algorithm, the hardware necessary to implement the algorithm is complicated due to the many multiplication operations required. Thus, in order to simplify the hardware, a signed LMS algorithm is used as in the following equation (4). EQU C.sub.k+1 =.beta.C.sub.k +.mu.e.sub.k sign(.gamma..sub.k) (4)
However, the adaptive algorithm requires a long processing time and has many hardware restrictions for real-time processing. That is, the hardware is complex, the performance is reduced due to low precision and processing latency, and real-time processing is difficult. Furthermore, since high-speed hardware is required so that high density recording may be performed at high-speed, a constant equalizer rather than the adaptive equalizer is used for high-speed processing. However, a recently developed adaptive equalizer (RAN-DFE) has a maximum processing speed of 48 Mbps.
Also, the conventional adaptive equalizer always requires a learning process for tracking and compensating the change of channels characteristics. As a result, data for learning is required for every sector and the data storage region is reduced by the amount of learning data required.